Natural (non-)informative priors for skew-symmetric distributions

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dc.contributor.authorDette, Holger
dc.contributor.authorLey, Christophe
dc.contributor.authorRubio, Francisco J.
dc.date.accessioned2016-05-11T11:28:49Z
dc.date.available2016-05-11T11:28:49Z
dc.date.issued2016
dc.description.abstractIn this paper, we present an innovative method for constructing proper priors for the skewness parameter in the skew-symmetric family of distributions. The proposed method is based on assigning a prior distribution on the perturbation effect of the skewness parameter, which is quantified in terms of the Total Variation distance. We discuss strategies to translate prior beliefs about the asymmetry of the data into an informative prior distribution of this class. We show that our priors induce posterior distributions with good frequentist properties via a Monte Carlo simulation study. We also propose a scale- and location-invariant prior structure for models with unknown location and scale parameters and provide sufficient conditions for the propriety of the corresponding posterior distribution. Illustrative examples are presented using simulated and real data.en
dc.identifier.urihttp://hdl.handle.net/2003/34962
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17010
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;23, 2016en
dc.subjectmeasure of skewnessen
dc.subjectWasserstein metricen
dc.subjecttotal variation distanceen
dc.subjectskew-symmetric distributionsen
dc.subjectprior elicitationen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleNatural (non-)informative priors for skew-symmetric distributionsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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